Volatility in stock return series of vietnam stock market

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY - oOo - NGUYỄN THỊ KIM NGÂN VOLATILITY IN STOCK RETURN SERIES OF VIETNAM STOCK MARKET MASTER THESIS Ho Chi Minh City – 2011 MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY o0o - NGUYỄN THỊ KIM NGÂN VOLATILITY IN STOCK RETURN SERIES OF VIETNAM STOCK MARKET MAJOR: BANKING AND FINANCE MAJOR CODE: 60.31.12 MASTER THESIS INSTRUCTOR: Dr VÕ XUÂN VINH Ho Chi Minh City – 2011 ACKNOWLEDGEMENT At first, I would like to show my sincerest gratitude to my supervisor, Dr Vo Xuan Vinh, for his valuable time and enthusiasm His whole-hearted guidance, encouragement and strong support during the time from the initial to the final phase are the large motivation for me to complete my thesis I also would like to thank all of my lecturers at Faculty of Banking and Finance, University of Economics Hochiminh City for their English program, knowledge and teaching during my master course at school In addition, my thanks also go to my beloved family for creating good and convenient conditions for me throughout all my studies at University as well as helping me overcome all the obstacles to finish this thesis Lastly, I offer my regards and blessings to all of those who supported me in any respects during the completion of the study i ABSTRACT This thesis studies the features of the stock return volatility and the presence of structural breaks in return variance of VNIndex in the Vietnam stock market by using the iterated cumulative sums of squares (ICSS) algorithm The relationship between Vietnam stock market’s volatility shifts and impacts of global crisis is also detected Using a long-span data, the results show that daily stock returns can be characterized by GARCH and GARCH in mean (GARCH-M) models while threshold GARCH (TGARCH) is not suitable About structural breaks, when applying ICSS to the standardized residuals filtered from GARCH (1, 1) model, the number of sudden jumps significantly decreases in comparison with the raw return series Events corresponding to those breaks and altering the volatility pattern of stock return are found to be country-specific Not any shifts are found during global crisis period In addition, because the research is not able to point out exactly what events caused sudden changes, the analysis on relationship between these information and shifts is just in relative meaning Further evidence also reveals that when sudden shifts are taken into account in the GARCH models, reduction in the volatility persistence is found It suggests that many previous studies may have overestimated the degree of volatility persistence existing in financial time series The small value of coefficients of the dummies representing breakpoints in modified GARCH model implies that the conditional variance of stock return is much affected by past trend of observed shocks and variance Our results have important implications regarding advising investors on decisions concerning pricing equity, portfolio investment and management, hedging and forecasting Moreover, it is also helpful for policy-makers in making and promulgating the financial policies ii TABLE OF CONTENTS ACKNOWLEDGEMENT i ABSTRACT ii TABLE OF CONTENTS iii LIST OF FIGURES v LIST OF TABLES vi ABBREVIATIONS vii 1: INTRODUCTION 2: LITERATURE REVIEW 2.1 Common characteristics of return series in the stock market 2.2 Volatility models suitable to the stock return characteristics 2.3 Identification of breakpoints in volatilities and influence of the regime changes 2.4 Events related to regime changes 2.5 Sudden changes in economic recession? 10 2.6 Overstatement of ICSS algorithm in raw returns series 10 3: HYPOTHESES 12 4: RESEARCH METHODS 13 4.1 Stationarity 13 4.2 Testing for stationarity 14 4.2.1 Autocorrelation diagram 14 4.2.2 Unit root test 15 4.3 GARCH model 16 4.3.1 ARMA 16 4.3.1.1 Moving average processes - MA(q) 17 4.3.1.2 Autoregressive processes - AR(p) 17 4.3.1.3 ARMA processes 18 4.3.1.4 Information criteria for ARMA model selection 19 4.3.2 ARCH & GARCH Model 20 4.3.2.1 ARCH Model 20 4.3.2.2 GARCH Model 21 4.4 TGARCH Model 22 4.5 GARCH-M model 23 iii 4.6 ICSS algorithm 24 4.7 Combination of GARCH model and sudden changes 26 5: DATA AND EMPIRICAL RESULTS 27 5.1 Data 27 5.2 Empirical results 29 5.2.1 Suitable models for stock return series of Vietnam 29 5.2.1.1 Choosing suitable ARMA model 29 5.2.1.2 Test for ARCH effect 30 5.2.1.3 GARCH models 31 5.2.2 Identification of break points and detection of related events 33 5.2.2.1 Breakpoints in raw returns 33 5.2.2.2 Breakpoints in filtered returns 38 5.2.2.3 Analysis of each volatility period 44 5.2.2.4 General comments on events and volatility corresponding to sudden changes detected by ICSS algorithm 57 5.2.3 Combined model after including dummies 57 6: CONCLUSION 60 Implications of the research 60 Limitations of the study 61 REFERENCE 62 APPENDIX 66 Table A1 Descriptive statistics of Vietnam stock market’s daily stock return 66 Table A2 Correlogram and Q-statistic of VNIndex daily rate of return 67 Table A3 Unit Root Test on VNIndex’s daily return 68 Table A4 Summary for estimation results of all ARMA models 69 Table A5 Statistically significant ARMA models with C constants 70 Table A6 Statistically significant ARMA models without C constants 72 Table A7 Estimation results of GARCH models 74 Table A8 Estimation results of GARCH-M models 77 Table A9 Estimation result of TGARCH model 79 Table A10 Estimation result of GARCH model modified with sudden changes 80 Table A11 ICSS code on WINRAT 81 iv LIST OF FIGURES Figure 5.1 Daily return series on HOSE 29 Figure 5.2 Structural breakpoints in volatility in raw returns 38 Figure 5.3 Structural breakpoints in volatility in filtered returns 39 v LIST OF TABLES Table 5.1 Descriptive statistics of Vietnam stock market’s daily return series 27 Table 5.2 Unit Root Test on VNIndex’s daily return 28 Table 5.3 Empirical results of different ARMA models 30 th Table 5.4 ARCH effect at lag 31 Table 5.5 Empirical results of different GARCH-family models 32 Table 5.6 Breakpoints detected by ICSS algorithm in the raw returns 33 Table 5.7 Breakpoints detected by ICSS algorithm in the filtered returns 40 vi ABBREVIATIONS CPI Consumer Price Index GARCH Generalized Autoregressive Conditional Heteroscedasticity GARCH-M GARCH in Mean GDP Gross Domestic Product HOSE Ho Chi Minh City Stock Exchange HOSTC Ho Chi Minh City Securities Trading Center ICSS algorithm Iterated Cumulative Sums of Squares algorithm SSC State Securities Committee of Vietnam TGARCH Threshold GARCH VND Vietnam Dong vii Volatility in Stock Return Series of Vietnam Stock Market ARMA(0,1) Dependent Variable: R Method: Least Squares Sample (adjusted): 3/04/2002 8/31/2010 Included observations: 2120 after adjustments Convergence achieved after iterations MA Backcast: 3/01/2002 C MA(1) R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Inverted MA Roots ARMA(1,2) Dependent Variable: R Method: Least Squares Sample (adjusted): 3/05/2002 8/31/2010 Included observations: 2119 after adjustments Convergence achieved after 16 iterations MA Backcast: 3/01/2002 3/04/2002 C AR(1) MA(1) MA(2) R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Inverted AR Roots Inverted MA Roots 71 Volatility in Stock Return Series of Vietnam Stock Market Table A6 Statistically significant ARMA models without C constants ARMA(1,0) NOT C Dependent Variable: R Method: Least Squares Sample (adjusted): 3/05/2002 8/31/2010 Included observations: 2119 after adjustments Convergence achieved after iterations AR(1) R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots ARMA (2, 0) _ NOT C Dependent Variable: R Method: Least Squares Sample (adjusted): 3/06/2002 8/31/2010 Included observations: 2118 after adjustments Convergence achieved after iterations AR(1) AR(2) R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots 72 Volatility in Stock Return Series of Vietnam Stock Market ARMA(0, 1) NOT C Dependent Variable: R Method: Least Squares Date: 11/04/10 Time: 20:39 Sample (adjusted): 3/04/2002 8/31/2010 Included observations: 2120 after adjustments Convergence achieved after iterations MA Backcast: 3/01/2002 MA(1) R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted MA Roots ARMA (1,2) _ NOT C Dependent Variable: R Method: Least Squares Sample (adjusted): 3/05/2002 8/31/2010 Included observations: 2119 after adjustments Convergence achieved after 18 iterations MA Backcast: 3/01/2002 3/04/2002 AR(1) MA(1) MA(2) R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots Inverted MA Roots 73 Volatility in Stock Return Series of Vietnam Stock Market Table A7 Estimation results of GARCH models GARCH(1,1) Dependent Variable: R Method: ML - ARCH (Marquardt) - Normal distribution Sample (adjusted): 3/05/2002 8/31/2010 Included observations: 2119 after adjustments Convergence achieved after 17 iterations MA Backcast: 3/01/2002 3/04/2002 Presample variance: backcast (parameter = 0.7) GARCH = C(4) + C(5)*RESID(-1)^2 + C(6)*GARCH(-1) AR(1) MA(1) MA(2) Variance Equation C 2.64E-063.45E-077.6658510.0000 RESID(-1)^20.3263870.02346813.908000.0000 GARCH(-1)0.7130860.01442849.424810.0000 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots Inverted MA Roots 74 Volatility in Stock Return Series of Vietnam Stock Market GARCH(2,1) Dependent Variable: R Method: ML - ARCH (Marquardt) - Normal distribution Sample (adjusted): 3/05/2002 8/31/2010 Included observations: 2119 after adjustments Convergence achieved after 19 iterations MA Backcast: 3/01/2002 3/04/2002 Presample variance: backcast (parameter = 0.7) GARCH = C(4) + C(5)*RESID(-1)^2 + C(6)*GARCH(-1) + C(7)*GARCH(-2) AR(1) MA(1) MA(2) Variance Equation C 3.06E-064.28E-077.1524490.0000 RESID(-1)^20.3974260.02975013.358910.0000 GARCH(-1)0.3386370.0725164.6698110.0000 GARCH(-2)0.3089320.0628494.9154970.0000 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots Inverted MA Roots 75 Volatility in Stock Return Series of Vietnam Stock Market GARCH(3,1) Dependent Variable: R Method: ML - ARCH (Marquardt) - Normal distribution Sample (adjusted): 3/05/2002 8/31/2010 Included observations: 2119 after adjustments Convergence achieved after 17 iterations MA Backcast: 3/01/2002 3/04/2002 Presample variance: backcast (parameter = 0.7) GARCH = C(4) + C(5)*RESID(-1)^2 + C(6)*GARCH(-1) + C(7)*GARCH(-2) + C(8)*GARCH(-3) AR(1) MA(1) MA(2) Variance Equation C 3.11E-064.52E-076.8869180.0000 RESID(-1)^20.4267670.03719711.473220.0000 GARCH(-1)0.3495120.0789554.4267500.0000 GARCH(-2)0.1347010.0745001.8080690.0706 GARCH(-3)0.1393510.0536292.5984280.0094 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots Inverted MA Roots 76 Volatility in Stock Return Series of Vietnam Stock Market Table A8 Estimation results of GARCH-M models GARCH-M (1,1) Dependent Variable: R Method: ML - ARCH (Marquardt) - Normal distribution Sample (adjusted): 3/05/2002 8/31/2010 Included observations: 2119 after adjustments Convergence achieved after 19 iterations MA Backcast: 3/01/2002 3/04/2002 Presample variance: backcast (parameter = 0.7) GARCH = C(5) + C(6)*RESID(-1)^2 + C(7)*GARCH(-1) GARCH AR(1) MA(1) MA(2) Variance Equation C 2.89E-063.55E-078.1218070.0000 RESID(-1)^20.3104500.02306813.458100.0000 GARCH(-1)0.7197160.01578145.607050.0000 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots Inverted MA Roots 77 Volatility in Stock Return Series of Vietnam Stock Market GARCH-M (2,1) Dependent Variable: R Method: ML - ARCH (Marquardt) - Normal distribution Sample (adjusted): 3/05/2002 8/31/2010 Included observations: 2119 after adjustments Convergence achieved after 22 iterations MA Backcast: 3/01/2002 3/04/2002 Presample variance: backcast (parameter = 0.7) GARCH = C(5) + C(6)*RESID(-1)^2 + C(7)*GARCH(-1) + C(8)*GARCH(-2) GARCH AR(1) MA(1) MA(2) Variance Equation C 3.09E-064.26E-077.2543020.0000 RESID(-1)^20.3454300.03454110.000470.0000 GARCH(-1)0.5283770.1129404.6783680.0000 GARCH(-2)0.1589630.0915131.7370440.0824 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots Inverted MA Roots 78 Volatility in Stock Return Series of Vietnam Stock Market Table A9 Estimation result of TGARCH model Dependent Variable: R Method: ML - ARCH (Marquardt) - Normal distribution Sample (adjusted): 3/05/2002 8/31/2010 Included observations: 2119 after adjustments Convergence achieved after 21 iterations MA Backcast: 3/01/2002 3/04/2002 Presample variance: backcast (parameter = 0.7) GARCH = C(4) + C(5)*RESID(-1)^2 + C(6)*RESID(-1)^2*(RESID(-1)1.358 ; else compute %maxent=0 end IncTiaoBreak * ************************************************************************* ****************** 81 Volatility in Stock Return Series of Vietnam Stock Market * * * * * * ICSS1and2 uses repeated calls to IncTiaoBreak to steps and to locate possible breaks between start and end It returns a VECT[INT] which will have dimension (if no significant break occurs), (if only a single break occurs), or (if there is more than one significant break, in which case, it returns the most extreme break values) * function ICSS1and2 x start end type vect[int] ICSS1and2 type series x type integer start end * local integer t1 t2 kstar kfirst klast * compute t1=start,t2=end @IncTiaoBreak x start end if %maxent==0 { dim ICSS1and2(0) return } compute t2=kstar=%maxent loop @IncTiaoBreak x t1 t2 if %maxent>0 compute t2=%maxent else break end loop compute kfirst=t2 compute t1=kstar+1,t2=end loop @IncTiaoBreak x t1 t2 if %maxent>0 compute t1=%maxent+1 else break end loop compute klast=t1-1 * if kfirst==klast compute ICSS1and2=||kfirst|| else compute ICSS1and2=||kfirst,klast|| end * ************************************************************************* ******** 82 Volatility in Stock Return Series of Vietnam Stock Market * * * * * * * This is the controlling routine for the ICSS algorithm Note that this does not the "pruning" step (step 3), just the complete enumeration from steps and Because the ICSS algorithm is recursive, and RATS doesn't allow recursive procedures, the recursion is unwound by using a stack of VEC[INT] for the starting and ending points being checked The stack is popped each time a call to ICSS1and2 returns either or breakpoint and pushed when it returns * procedure ICSS x start end type series x type integer start end * local vect[int] breaks local vect[int] newbreaks local vect[int] t1 t2 local vect temp local integer i k local series xc local integer startl endl * inquire(series=x) startl